Seven of fourteen models named VWO first on the direct prompt; one named Optimizely CMS. Both were named by all fourteen models and VWO carries 65 labels and Optimizely CMS 61, so the shares are not directly comparable.
Named in two categories this edition.
By Optimizely, San Francisco, United States, founded 2010. Named in six categories this edition.
Share is the count of first choices across the direct, paraphrase, budget and scale prompts over all fourteen models, for a mid-market B2B company; rank is within the category; every quote names the model and the prompt it came from. Both figures come from the experimentation and personalization page.
Across every category in the November 2026 Edition, VWO and Optimizely CMS were named in the same answer 132 times, of the 167 answers naming VWO and the 259 naming Optimizely CMS. In those answers Optimizely CMS took the first choice twenty-two times and VWO thirty-one.
| Model | Direct | Paraphrase | Comparative | Budget-constrained | Scale-constrained | Negative |
|---|---|---|---|---|---|---|
| Claude Haiku 4.5 | ||||||
| GPT-5.4 mini | ||||||
| Gemini 3.5 Flash | ||||||
| Perplexity Sonar | ||||||
| Grok 4.20 | ||||||
| Mistral Small | ||||||
| DeepSeek V4 Flash | ||||||
| Llama 4 Maverick | ||||||
| Qwen 3.7 Flash | ||||||
| Kimi K2 | ||||||
| GLM 4.7 FlashX | ||||||
| MiniMax M2.5 | ||||||
| GPT-6 Luna | ||||||
| Muse Glimmer 30B |
Bold names in an answer are the products the judge labeled a first choice; a model naming several gives each of them that label. The full answer text for every row is in the record.
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“Avoid VWO if page speed and user trust are critical, or if you have limited technical support.” GLM 4.7 FlashX · negative prompt · hard negative
“the reliability and support issues make it risky for serious experimentation” DeepSeek V4 Flash · negative prompt · hard negative
“its current pricing page directs buyers to request a demo rather than showing a price, so it may be harder to budget for upfront” GPT-6 Luna · budget prompt · soft negative
“platforms like VWO or Optimizely are commonly considered in this middle market, depending on whether you prioritize faster time-to-value (VWO)” Claude Haiku 4.5 · scale prompt · first choice
“The Best All-Rounder: VWO (Visual Website Optimizer) ... widely considered the best balance of power and usability for mid-sized companies” Qwen 3.7 Flash · paraphrase prompt · first choice
“Tight budget, marketer-led, low traffic, needs CRM bucketing: Convert Experiences or VWO Experience Optimization.” Muse Glimmer 30B · paraphrase prompt · first choice
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“you should exercise significant caution with Optimizely, particularly regarding its cost structure, support quality, and technical performance” Qwen 3.7 Flash · negative prompt · hard negative
“Avoid beginning with Optimizely, Adobe Target, Kameleoon, or AB Tasty unless the company has enterprise-level requirements and budget” Perplexity Sonar · budget prompt · hard negative
“Avoid for Mid-Sized B2B (unless budget is large): Optimizely — median spend is ~$81K/year” DeepSeek V4 Flash · paraphrase prompt · hard negative
“Optimizely is among the most common "default shortlists" for companies seeking rigorous testing, large-scale collaboration, and a mature measurement stack.” Claude Haiku 4.5 · comparative prompt · first choice
“Optimizely is described as an industry-leading experimentation platform that enables businesses to test and optimize digital experiences” Muse Glimmer 30B · comparative prompt · first choice
“Choose Optimizely if you want a large enterprise suite with strong experimentation + personalization + analytics integration.” GPT-5.4 mini · comparative prompt · first choice
Comparisons are drawn for the top eight products in each category, each against each. The output is the models' output; nothing here is a recommendation by the index.